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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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Gene Expression Mining in Type 2 Diabetes Research.

Donald R Dunbar1

  • 1Edinburgh Genomics, University of Edinburgh, Edinburgh, UK. donald.dunbar@ed.ac.uk.

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2019
PubMed
Summary
This summary is machine-generated.

Gene expression analysis using microarray and next-generation sequencing generates valuable data. This study highlights free, web-based tools for gene enrichment, literature mining, and transcription factor analysis to fully exploit these findings.

Keywords:
BioinformaticsData miningGene enrichmentGene expressionLiterature miningMicroarrayNext generation sequencingTranscription factor binding site

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression analysis via microarray and next-generation sequencing (NGS) is fundamental to modern biomedical research.
  • Vast amounts of data generated require specialized computational techniques for comprehensive interpretation.
  • Effective data mining is crucial for maximizing the value derived from gene expression experiments.

Purpose of the Study:

  • To introduce novice users to essential data mining techniques for gene expression data.
  • To describe publicly available, web-based tools for analyzing microarray and NGS data.
  • To focus on gene enrichment analysis, literature mining, and transcription factor binding site analysis.

Main Methods:

  • Review and description of free, web-based bioinformatics tools.
  • Focus on tools applicable to gene enrichment analysis.
  • Exploration of literature mining and transcription factor binding site analysis tools.

Main Results:

  • Identification of accessible tools for key gene expression data analysis areas.
  • Emphasis on user-friendly, publicly available resources for researchers.
  • Demonstration of how these tools aid in the interpretation of complex gene expression datasets.

Conclusions:

  • Free, web-based tools can significantly enhance the analysis of gene expression data.
  • Novice users can leverage these resources for gene enrichment, literature mining, and transcription factor analysis.
  • These tools empower researchers to fully exploit the potential of microarray and NGS experiments.